Dean Adams, Iowa State University
16 October, 2019
StereoMorph may be used for symmetry-based estimation of missing landmarks\(\small{D}_{Proc}= 0.009\). Pretty good!
Test Procedure
\(\small{D}_{Proc_{Ref-Orig}} = 0.26\) \(\small{D}_{Proc_{Ref-Est}} = 0.23\)
\(\small{D}_{Proc_{Orig-Est}} = 0.13\) Not good at all!
Test Procedure
\(\small{D}_{Proc_{Ref-Orig}} = 0.15\) \(\small{D}_{Proc_{Ref-Est}} = 0.13\)
\(\small{D}_{Proc_{Orig-Est}} = 0.08\) Not good at all!
\(\small{D}_{Proc_{Ref-Orig}} = 0.26\) \(\small{D}_{Proc_{Ref-Est}} = 0.27\)
\(\small{D}_{Proc_{Orig-Est}} = 0.003\) MUCH Better!
\(\small{D}_{Proc_{Ref-Orig}} = 0.15\) \(\small{D}_{Proc_{Ref-Est}} = 0.15\)
\(\small{D}_{Proc_{Orig-Est}} = 0.011\) MUCH Better!
Advantages - Exploits spatial relationships of anatomy within a specimen
Disadvantages - Less accurate if many landmarks in a region missing (common with fossils) - Does not leverage additional covariation information in sample
Use covariation between landmarks to estimate locations
Procedure
\(\small{D}_{Proc_{Ref-Orig}} = 0.26\) \(\small{D}_{Proc_{Ref-Est}} = 0.27\)
\(\small{D}_{Proc_{Orig-Est}} = 0.030\) Pretty good!
\(\small{D}_{Proc_{Ref-Orig}} = 0.15\) \(\small{D}_{Proc_{Ref-Est}} = 0.15\)
\(\small{D}_{Proc_{Orig-Est}} = 0.011\) Even Better!
Advantages - Exploits spatial relationships of anatomy within a specimen - Leverages covariation between anatomical landmarks - Leverages covariation within a sample
Disadvantages - May be less accurate when small samples are examined
HIgher error with \(\small{\uparrow}\) missing landmarks and \(\small{\uparrow}\) specimens containing missing data
Regression method generally preferred
Missing data has been a major challenge to GM analyses
Deleting specimens or landmarks ignores information
Morphometric-based estimation
‘Classical’ statistical approaches extended to morphometrics
Regression approach appears most robust